Yiming Xu

Speeding up the promotion and application of bio-fuel ethanol was a national strategy in China, which in turn affected changes in the raw material planting structure. This study analyzed the distribution of nitrogen and phosphorus forms in water bodies and the soil of the typical maize and cassava fuel ethanol raw material planting areas. The results revealed that the maize planting area faced more serious TN and TP pollution. The river pollution was greatly affected by TN, TP, Ex-P and Fe/Al-P in soil, while soil TN and NO₃⁻-N were the main factors influencing its counterpart. Furthermore, the risk assessment of soil nitrogen and phosphorus loss was carried out based on planting structures of crops. We investigated whether the water quality indexes or soil nitrogen and phosphorus loss risk assessment results showed that the Yujiang River stayed significantly less polluted. It was proven that the cassava planting area was more suitable for vigorously developing fuel ethanol. As for the high-risk areas, ecological agriculture promoting and fertilizer controlling measures were suggested. Under the change of the fuel-ethanol policy, this study could provide scientific support for the assessment of the impact of the Chinese national fuel ethanol policy on the water environment of the raw material planting area.

Mapping the SOC distributions in coastal wetlands plays an important role in assessing ecosystem services, predicting the greenhouse effects and investigating global carbon cycle. Few research has explored the relationships of SOC and environmental variables with seasonal changes, and the effects of multi-temporal environmental variables on Digital Soil Mapping (DSM). The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in wet season showed stronger correlations and higher importance scores with SOC compared with those in dry season. In addition, SOC prediction models based on multi-temporal data in wet season and mono-temporal data in April had stronger prediction performance compared with those based on multi-temporal data in dry season. As a result, data fusion of multi-temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil-landscape attributes and spectral characteristics in coastal wetlands of the YRD in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models based on wet season than those based on dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil-landscape relationships. In general, the findings of this research demonstrated that the selection of the environmental variables in the establishment of DSM model should consider the seasonal effects of environmental variables.

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K_ex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.